PROJECT SUMMARY/ABSTRACT: InSight Surgical Technologies, LLC, is a newly formed start-up founded by senior members of a long-standing NIH-funded research effort to develop intraoperative image updating that maintains accurate image correspondence with the surgical field throughout a procedure. The effort has led to research innovations that have generated an intellectual property (IP) portfolio of 8 issued and 10 pending patents. In this Phase I SBIR application, feasibility of new algorithms for assembling multiple intraoperative stereovision (iSV) acquisitions into a composite field-of-view and accomplishing iSV-to-MRI registration/re-registration will be developed along with conversion of existing iSV algorithms for 3D surface reconstruction and calibration in graphics processing unit (GPU) accelerated forms. Specific aims will (1) develop algorithms to (i) assimilate partially and/or fully overlapping iSV acquisitions into a single field-of-view, and (ii) achieve iSV to preoperative MRI (pMR) registration by incorporating closed cranium iSV-to-pMR alignment as an approximation for high-fidelity intraoperative re-registration post craniotomy, (2) implement GPU versions of already-optimized iSV calibration and 3D surface reconstruction software under quality systems in partnership with ArrayFire and prepare for GPU conversion of new software algorithms developed in Aim 1, and (3) evaluate Aim 1 and Aim 2 software to show it meets accuracy attained with research grade codes or exceeds standard clinical methods using laboratory and clinical data, retrospectively, and animal experiments, prospectively. Successful completion of Phase I will set the stage for Phase II during which new Aim 1 algorithms will join Aim 2 codes as finalized commercial-grade GPU software under design control, validation, procedural use and quality systems sufficient for FDA regulatory filing, accompanied by similar generation of software for updated MRI views (uMR). Multi- site, multi-surgeon use data will be collected to establish clinically-relevant and acceptable image-updating platform performance in terms of ease-of-use, efficiency and accuracy.